It would be very difficult even for a resident to characterise the social dynamics of a city and to reveal to foreigners the evolving activity patterns which occur in its various areas. To address this problem, however, large amount of data produced by location-based social networks (LBSNs) can be exploited and combined effectively with techniques of user profiling. The key idea we introduce in this demo is to improve city areas and venues classification using semantics extracted both from places and from the online profiles of people who frequent those places. We present the results of our methodology in LiveCities, a web application which shows the hidden character of several italian cities through clustering and information visualisations paradigms. In particular we give in-depth insights of the city of Florence, IT, for which the majority of the data in our dataset have been collected. The system provides personal recommendation of areas and venues matching user interests and allows the free exploration of urban social dynamics in terms of people lifestyle, business, demographics, transport etc. with the objective to uncover the real `pulse' of the city. We conducted a qualitative validation through an online questionnaire with 28 residents of Florence to understand the shared perception of city areas by its inhabitants and to check if their mental maps align to our results. Our evaluation shows how considering also contextual semantics like people profiles of interests in venues categorisation can improve clustering algorithms and give good insights of the endemic characteristics and behaviours of the detected areas.

LiveCities: Revealing the Pulse of Cities by Location-based Social Networks Venues and Users Analysis / Alberto Del Bimbo; Andrea Ferracani; Daniele Pezzatini; Federico D'Amato; Martina Sereni. - ELETTRONICO. - (2014), pp. 163-166. (Intervento presentato al convegno International World Wide Web Conference nel 2014) [10.1145/2567948.2577035].

LiveCities: Revealing the Pulse of Cities by Location-based Social Networks Venues and Users Analysis

DEL BIMBO, ALBERTO;FERRACANI, ANDREA;PEZZATINI, DANIELE;
2014

Abstract

It would be very difficult even for a resident to characterise the social dynamics of a city and to reveal to foreigners the evolving activity patterns which occur in its various areas. To address this problem, however, large amount of data produced by location-based social networks (LBSNs) can be exploited and combined effectively with techniques of user profiling. The key idea we introduce in this demo is to improve city areas and venues classification using semantics extracted both from places and from the online profiles of people who frequent those places. We present the results of our methodology in LiveCities, a web application which shows the hidden character of several italian cities through clustering and information visualisations paradigms. In particular we give in-depth insights of the city of Florence, IT, for which the majority of the data in our dataset have been collected. The system provides personal recommendation of areas and venues matching user interests and allows the free exploration of urban social dynamics in terms of people lifestyle, business, demographics, transport etc. with the objective to uncover the real `pulse' of the city. We conducted a qualitative validation through an online questionnaire with 28 residents of Florence to understand the shared perception of city areas by its inhabitants and to check if their mental maps align to our results. Our evaluation shows how considering also contextual semantics like people profiles of interests in venues categorisation can improve clustering algorithms and give good insights of the endemic characteristics and behaviours of the detected areas.
2014
Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion
International World Wide Web Conference
2014
Alberto Del Bimbo; Andrea Ferracani; Daniele Pezzatini; Federico D'Amato; Martina Sereni
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/957176
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